Real Time Hand Tracking, Pose Classification and Interface Control

ABSTRACT

A hand gesture from a camera input is detected using an image processing module of a consumer electronics device. The detected hand gesture is identified from a vocabulary of hand gestures. The electronics device is controlled in response to the identified hand gesture. This abstract is not to be considered limiting, since other embodiments may deviate from the features described in this abstract.

CROSS REFERENCE TO RELATED DOCUMENTS

This application claims priority to and claims the benefit of U.S.Provisional Patent Application Ser. No. 61/258,975 titled “REAL TIMEHAND TRACKING AND POSE CLASSIFICATION USING SIFT AND KLT,” which wasfiled in the United States Patent Office on Nov. 6, 2009, and which isincorporated herein by reference in its entirety.

COPYRIGHT AND TRADEMARK NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction of the patent document or thepatent disclosure, as it appears in the Patent and Trademark Officepatent file or records, but otherwise reserves all copyright rightswhatsoever. Trademarks are the property of their respective owners.

BACKGROUND

A hand presents a motion of twenty seven (27) degrees of freedom (DOF).Of the twenty seven degrees of freedom, twenty one (21) represent jointangles and six (6) represent orientation and location. Hand trackingconventionally utilizes colored gloves and color pattern matching,retro-reflective markers attached to a hand using an array ofoverlapping cameras (e.g., stereoscopic camera systems), or instrumentedgloves/sensor systems.

BRIEF DESCRIPTION OF THE DRAWINGS

Certain illustrative embodiments illustrating organization and method ofoperation, together with objects and advantages may be best understoodby reference detailed description that follows taken in conjunction withthe accompanying drawings in which:

FIG. 1 is a diagram of an example of an implementation of a televisioncapable of performing automated real time hand tracking, poseclassification, and interface control consistent with certainembodiments of the present invention.

FIG. 2 is a block diagram of an example core processing module thatprovides automated real time hand tracking, pose classification, andinterface control in association with the television of FIG. 1consistent with certain embodiments of the present invention.

FIG. 3 is a flow chart of an example of an implementation of a processthat provides automated real time hand tracking, pose classification,and interface control consistent with certain embodiments of the presentinvention.

FIG. 4 is a flow chart of an example of an implementation of a processthat provides training processing associated with automated real timehand tracking, pose classification, and interface control consistentwith certain embodiments of the present invention.

FIG. 5 is a flow chart of an example of an implementation of a processthat provides detection and pose recognition processing associated withautomated real time hand tracking, pose classification, and interfacecontrol consistent with certain embodiments of the present invention.

FIG. 6 is a flow chart of an example of an implementation of a processthat provides electronic device user interface processing associatedwith automated real time hand tracking, pose classification, andinterface control consistent with certain embodiments of the presentinvention.

FIG. 7 is a flow chart of an example of an implementation of a processthat provides electronic device user interface processing and poseassignment to control functions of an electronic device associated withautomated real time hand tracking, pose classification, and interfacecontrol consistent with certain embodiments of the present invention.

DETAILED DESCRIPTION

While this invention is susceptible of embodiment in many differentforms, there is shown in the drawings and will herein be described indetail specific embodiments, with the understanding that the presentdisclosure of such embodiments is to be considered as an example of theprinciples and not intended to limit the invention to the specificembodiments shown and described. In the description below, likereference numerals are used to describe the same, similar orcorresponding parts in the several views of the drawings.

The terms “a” or “an,” as used herein, are defined as one or more thanone. The term “plurality,” as used herein, is defined as two or morethan two. The term “another,” as used herein, is defined as at least asecond or more. The terms “including” and/or “having,” as used herein,are defined as comprising (i.e., open language). The term “coupled,” asused herein, is defined as connected, although not necessarily directly,and not necessarily mechanically. The term “program” or “computerprogram” or similar terms, as used herein, is defined as a sequence ofinstructions designed for execution on a computer system. A “program,”or “computer program,” may include a subroutine, a function, aprocedure, an object method, an object implementation, in an executableapplication, an applet, a servlet, a source code, an object code, ashared library/dynamic load library and/or other sequence ofinstructions designed for execution on a computer system having one ormore processors.

The term “program,” as used herein, may also be used in a second context(the above definition being for the first context). In the secondcontext, the term is used in the sense of a “television program.” Inthis context, the term is used to mean any coherent sequence of audiovideo content such as those which would be interpreted as and reportedin an electronic program guide (EPG) as a single television program,without regard for whether the content is a movie, sporting event,segment of a multi-part series, news broadcast, etc. The term may alsobe interpreted to encompass commercial spots and other program-likecontent which may not be reported as a program in an electronic programguide.

Reference throughout this document to “one embodiment,” “certainembodiments,” “an embodiment,” “an implementation,” “an example” orsimilar terms means that a particular feature, structure, orcharacteristic described in connection with the example is included inat least one embodiment of the present invention. Thus, the appearancesof such phrases or in various places throughout this specification arenot necessarily all referring to the same embodiment. Furthermore, theparticular features, structures, or characteristics may be combined inany suitable manner in one or more embodiments without limitation.

The term “or” as used herein is to be interpreted as an inclusive ormeaning any one or any combination. Therefore, “A, B or C” means “any ofthe following: A; B; C; A and B; A and C; B and C; A, B and C.” Anexception to this definition will occur only when a combination ofelements, functions, steps or acts are in some way inherently mutuallyexclusive.

The present subject matter provides automated real time hand tracking,pose classification, and interface control. The present subject mattermay be used in association with systems that identify and categorizehand poses and hand pose changes for a bare hand. The present subjectmatter may also be used in association with user interface controlsystems to allow hand gestures to control a device, such as a consumerelectronics device. The real time hand tracking, pose classification,and interface control described herein is further adaptable to allowuser formation of input controls based upon hand gestures. Additionally,hand characteristics of each individual user of a user interface system,such as characteristics resulting from injury or other characteristics,may be processed and configured in association with gesture-basedcontrol of consumer electronics devices to allow individualizedautomated recognition of hand gestures to control common or differentuser interface controls. Many other possibilities exist for real timehand tracking, pose classification, and interface control and all areconsidered within the scope of the present subject matter.

Example detected hand gestures that may be identified and used tocontrol a device, such as a consumer electronics device, includedetection of a “thumbs-up” hand gesture or a “pointing-up” hand gesturethat may be identified and associated with a control command to turn ona consumer electronics device. Similarly, detection of a “thumbs-down”or a “pointing-down” hand gesture, for example, may be identified andassociated with a control command to turn off a consumer electronicsdevice. Any hand gesture that may be detected and identified based uponthe present subject matter may be used to control an interface, such asa user interface, of a device. Additionally, hand gestures may becreated by a user and assigned to control functions in response to handgesture inputs. Many possibilities exist for user interface control andall are considered within the scope of the present subject matter.

The present subject matter may operate using a single camera, such as amonocular camera, and a data driven approach that uses scale invariantfeature transforms (SIFT) descriptors and pixel intensity/displacementdescriptors as extracted features to not only track but also classifyarticulated poses of a hand in three dimensions. However, it should benoted that the processing described herein may be extended to usemultiple cameras which may dramatically increase accuracy. The real timeaspect allows it to be integrated into consumer electronic devices. Itmay also have application in three dimensional (3D) modeling, newdesktop user-interfaces, and multi touch interfaces. Real-time embeddedsystems may also be improved by creating a more intuitive interfacedevice for such implementations.

SIFT is a technique for processing images that extracts salient featuredescriptors that are invariant to rotation, translation, scaling. Assuch, SIFT descriptors may be considered robust for matching,recognition, and image registration tasks. Pixel intensity/displacementis a technique for processing images that uses pixel intensity andlocality of displacement of pixels in relation to its neighboring pixelsto track pixels within images. Features to track within a sequence ofimages are those pixels that are determined by calculating an imagegradient between one image and the same image displaced by a known valueand forming an image gradient matrix. If the Eigen values of the imagesgradient matrix are greater than a specified threshold, such as forexample a magnitude ten (10.0), each such feature may be considered afeature that provides information suitable for tracking purposes.Kanade, Lucas, and Tomasi (KLT) descriptors represent one possible formof pixel intensity/displacement descriptors that may be used. However,it is understood that any form of pixel intensity/displacementdescriptors may be used as appropriate for a given implementation.

The tracking aspect may include tracking out-of-plane rotations andother characteristics of a hand in motion. The classified articulatedposes of a hand in three dimensions may be associated with userinterface controls for consumer electronics devices. A configuration andtraining mode allows customized pose orientations to be associated withspecific controls for an electronic system. Because bare-hand trackingand pose recognition is performed using a single camera, conventionaltechniques that utilize retro-reflective markers, arrays of cameras, orother conventional techniques are not needed. Further, resolution andscope may be maintained while performing the hand tracking, poseclassification, and interface control in real time.

The subject matter described herein may be utilized to capture increaseddegrees-of-freedom, enabling direct manipulation tasks and recognitionof an enhanced set of gestures when compared with certain conventionaltechnologies. The approach described herein illustrates examples of adata-driven approach that allows a single frame to be used to correctlyidentify a pose, based upon a reduced set of stored pose information.Robust scale invariant features are extracted from a single frame of ahand pose and a multiclass support vector machine (SVM) is utilized toclassify the pose in real time. Multiple-hypothesis inference isutilized to allow real time bare-hand tracking and pose recognition.

The present subject matter facilitates real time performance by use of achoice of image features and by use of multiclass SVM to infer a closestpose image that allows rapid retrieval of a closest match. Regarding thechoice of image features, both SIFT and pixel intensity/displacementfeatures may be calculated rapidly and the multiclass SVM may usesimilar filters to extract salient information to expedite extractionspeed. Because the multiclass SVM is trained on prior image sets,retrieval rate may be further improved. Additional details of theprocessing performed in association with the present subject matter willbe described following some introductory example architectures uponwhich the present subject matter may be implemented.

Turning now to FIG. 1, FIG. 1 is a diagram of an example of animplementation of a television 100 capable of performing automated realtime hand tracking, pose classification, and interface control. Itshould be noted that use of the television 100 within the presentexample is for purposes of illustration only. As such, a system thatimplements the automated real time hand tracking, pose classification,and interface control described herein may form a portion of a handheldconsumer electronics device or any other suitable device withoutdeparture from the scope of the present subject matter.

An enclosure 102 houses a display 104 that provides visual and/or otherinformation to a user of the television 100. The display 104 may includeany type of display device, such as a cathode ray tube (CRT), liquidcrystal display (LCD), light emitting diode (LED), projection or otherdisplay element or panel. The display 104 may also include a touchscreendisplay, such as a touchscreen display associated with a handheldconsumer electronics device or other device that includes a touchscreeninput device.

An infrared (IR) (or radio frequency (RF)) responsive input device 106provides input capabilities for the user of the television 100 via adevice, such as an infrared remote control device (not shown). An audiooutput device 108 provides audio output capabilities for the television100, such as audio associated with rendered content. The audio outputdevice 108 may include a pair of speakers, driver circuitry, andinterface circuitry as appropriate for a given implementation.

A light emitting diode (LED) output module 110 provides one or moreoutput LEDs and associated driver circuitry for signaling certain eventsor acknowledgements to a user of the television 100. Many possibilitiesexist for communicating information to a user via LED signaling and allare considered within the scope of the present subject matter.

A camera 112 provides image capture capabilities for the television 100.Images captured by the camera 112 may be processed, as described in moredetail below, to perform the automated real time hand tracking, poseclassification, and interface control associated with the presentsubject matter.

FIG. 2 is a block diagram of an example core processing module 200 thatprovides automated real time hand tracking, pose classification, andinterface control in association with the television 100 of FIG. 1. Thecore processing module 200 may be integrated into the television 100 orimplemented as part of a separate interconnected module as appropriatefor a given implementation. A processor 202 provides computerinstruction execution, computation, and other capabilities within thecore processing module 200. The infrared input device 106 is shown andagain provides input capabilities for the user of the television 100 viaa device, such as an infrared remote control device (again not shown).

The audio output device 108 is illustrated and again provides audiooutput capabilities for the core processing module 200. The audio outputdevice 108 may include one or more speakers, driver circuitry, andinterface circuitry as appropriate for a given implementation.

A tuner/decoder module 204 receives television (e.g., audio/video)content and decodes that content for display via the display 104. Thecontent may include content formatted either via any of the motionpicture expert group (MPEG) standards, or content formatted in any othersuitable format for reception by the tuner/decoder module 204. Thetuner/decoder module 204 may include additional controller circuitry inthe form of application specific integrated circuits (ASICs), antennas,processors, and/or discrete integrated circuits and components forperforming electrical control activities associated with thetuner/decoder module 204 for tuning to and decoding content receivedeither via wireless or wired connections to the core processing module200. The display 104 is illustrated and again provides visual and/orother information for the core processing module 200 via thetuner/decoder module 204.

A communication module 206 may alternatively provide communicationcapabilities for the core processing module 200, such as for retrievalof still image content, audio and video content, or other content via asatellite, cable, storage media, the Internet, or other contentprovider, and other activities as appropriate for a givenimplementation. The communication module 206 may support wired orwireless standards as appropriate for a given implementation. Examplewired standards include Internet video link (IVL) interconnection withina home network, for example, such as Sony Corporation's Bravia® InternetVideo Link (BIVL™) Example wireless standards include cellular wirelesscommunication and Bluetooth® wireless communication standards. Manyother wired and wireless communication standards are possible and allare considered within the scope of the present subject matter.

A memory 208 includes a hand pose storage area 210, a hand tracking andpose processing storage area 212, and a control correlation storage area214. The hand pose storage area 210 may store information, such as avocabulary of hand poses captured and utilized for processing theautomated real time hand tracking, pose classification, and interfacecontrol of the present subject matter. The hand tracking and poseprocessing storage area 212 may store information, such as imagescaptured by the camera 112 and intermediate and final stages ofprocessing of captured images in association with hand poseidentification. The control correlation storage area 214 may storeinformation, such hand positions or hand position identifiers that havebeen correlated with control commands for the television 100.

It is understood that the memory 208 may include any combination ofvolatile and non-volatile memory suitable for the intended purpose,distributed or localized as appropriate, and may include other memorysegments not illustrated within the present example for ease ofillustration purposes. For example, the memory 208 may include a codestorage area, a code execution area, and a data area without departurefrom the scope of the present subject matter.

A hand tracking and pose processing module 216 is also illustrated. Thehand tracking and pose processing module 216 provides processingcapabilities for the core processing module 200 to perform the automatedreal time hand tracking, pose classification, and interface control, asdescribed above and in more detail below. The camera 112 is illustratedand again provides image capture capabilities for the core processingmodule 200.

It should be noted that the modules described above in association withthe core processing module 200 are illustrated as component-levelmodules for ease of illustration and description purposes. It is alsounderstood that these modules include any hardware, programmedprocessor(s), and memory used to carry out the respective functions ofthese modules as described above and in more detail below. For example,the respective modules may include additional controller circuitry inthe form of application specific integrated circuits (ASICs),processors, and/or discrete integrated circuits and components forperforming electrical control activities. Additionally, the modules mayinclude interrupt-level, stack-level, and application-level modules asappropriate. Furthermore, the modules may include any memory componentsused for storage, execution, and data processing by these modules forperforming the respective processing activities.

It should also be noted that the hand tracking and pose processingmodule 216 may form a portion of other circuitry described withoutdeparture from the scope of the present subject matter. Further, thehand tracking and pose processing module 216 may alternatively beimplemented as an application stored within the memory 208. In such animplementation, the hand tracking and pose processing module 216 mayinclude instructions executed by the processor 202 for performing thefunctionality described herein. The processor 202 may execute theseinstructions to provide the processing capabilities described above andin more detail below for the core processing module 200. The handtracking and pose processing module 216 may form a portion of aninterrupt service routine (ISR), a portion of an operating system, aportion of a browser application, or a portion of a separate applicationwithout departure from the scope of the present subject matter.

The processor 202, the infrared input device 106, the audio outputdevice 108, the tuner/decoder module 204, the communication module 206,the memory 208, the camera 112, and the hand tracking and poseprocessing module 216 are interconnected via one or moreinterconnections shown as interconnection 218 for ease of illustration.The interconnection 218 may include a system bus, a network, or anyother interconnection capable of providing the respective componentswith suitable interconnection for the respective purpose.

The processing described herein includes certain categories ofactivities. A robust feature set for hand detection and pose inferenceis extracted and stored. Trained multiclass SVM is used to infer a posetype. The articulated pose is then approximated using inverse kinematics(IK) optimization. Each of these processing aspects will be described inmore detail below.

Extraction and Storage of Feature Set

Regarding extraction and storage of a robust feature set for handdetection and pose inference, an improvised flock of features trackingalgorithm may be used to track a region of interest (ROI) betweensubsequent video frames. Flock of features tracking may be used forrapid tracking of non-rigid and highly articulated objects such ashands. Flock of features tracking combines pixel intensity/displacementfeatures and learned foreground color distribution to facilitate twodimensional (2D) tracking. Flock of features tracking further triggersSIFT features extraction. The extracted SIFT features may be used forpose inference. Flock of features tracking assumes that salient featureswithin articulated objects move from frame to frame in a way similar toa flock of birds. The path is calculated using an optical flowalgorithm.

Additional conditions or constraints may be utilized in certainimplementations, such as for example, that all features maintain aminimum distance from each other, and that such features never exceed adefined distance from a feature median. Within such an implementation,if the condition or constraint is violated, the location of the featuresmay be recalculated and positioned based upon regions that have a highresponse to skin color filtering. The flock of features behaviorimproves the tracking of regions of interest across frame transitions,and may further improve tracking for situations where the appearance ofregion may change over time. The additional cue on skin color allowsadditional information that may be used when features are lost across asequence of frames.

Pixel intensity/displacement features are extracted by measuringbrightness gradients in multiple directions across the image, a stepthat is closely related to finding oriented gradient when extractingSIFT descriptors. In combination with generated image pyramids, afeature's image area may be matched efficiently to a “most” similar areawithin a search window in the following video frame. An image pyramidmay be considered a series of progressively smaller-resolutioninterpolations generated based upon the original image, such as byreducing grayscale within an image by configured percentages (e.g., tenpercent (10%)) for iterations of processing probabilities from histogramdata of a hand as described in more detail below. The feature sizedetermines the amount of context knowledge that may be used formatching. If the feature match correlation between two consecutiveframes is below a configurable threshold, the feature may be considered“lost.” As such, configurable thresholds allow resolution adjustment fortracking and identification purposes.

The generated image pyramids may be used to extract both pixelintensity/displacement and SIFT features. Pixel intensity/displacementfeatures may be considered appropriate for tracking purposes. However itis recognized that pixel intensity/displacement features are notinvariant to scale or rotation and, as such, are not utilized to inferthe hand poses due to accuracy. SIFT features are invariant to imagescaling and rotation, and at least partially invariant to change inillumination and 2D camera viewpoint. SIFT features are also welllocalized in both spatial and frequency domains, which may reduce aprobability of disruption by occlusion, clutter, noise, or otherfactors.

The time impact of extracting the pixel intensity/displacement and SIFTfeatures may be reduced by use of a cascade filtering approach, in whichmore time-costly operations are applied at locations that pass aninitial test. The initial test may involve, for example, dividing theimage into thirty two by thirty two (32×32) pixel sub-windows. For eachsub-window, keypoints may be calculated using a difference of Gaussianfilter. If there are many keypoints in any sub-window, then the completeSIFT descriptor may be calculated. Otherwise, the sub-window may bediscarded to eliminate large portions of the image that may not berelevant for hand position detection. SIFT descriptors were chosen forthis implementation because SIFT descriptors transform image data intoscale-invariant coordinates relative to local features.

Transformation of image data using SIFT descriptors into scale-invariantcoordinates relative to local features involves four stages. A firststage includes scale-space extrema detection. A second stage includeskeypoint localization. The third stage includes orientation assignment.The fourth stage includes keypoint descriptor transformation.

Regarding scale-space extrema detection, scale-space extrema detectionincludes a computational search over all scales and image locations.Scale-space extrema detection may be implemented, for example, using adifference-of-Gaussian filter.

Regarding keypoint localization, for each candidate location identifiedvia the scale-space extrema detection, a detailed model is fit todetermine location and scale. Keypoints are selected based on measuresof their stability within the image or sequence of images. The stabilitywithin the image or sequence of images may be defined as keypoints thathave high contrast between themselves and their neighboring pixels. Thisstability may be used to decrease or remove sensitivity to low contrastinterest points that may be sensitive to noise or that may be poorlylocalized along edges.

Regarding orientation assignment, one or more orientations are assignedto each keypoint location identified via the keypoint localization basedon local image gradient directions. All future operations may beperformed on image data that has been transformed relative to theassigned orientation, scale, and location for each feature, therebyproviding invariance to these transformations.

Regarding keypoint descriptor transformation, the local image gradientsresulting from the orientation assignment are measured at the selectedscale in the region around each keypoint. The local image gradients maythen be transformed into a representation that allows for significantlevels of local shape distortion and change in illumination.

An interesting aspect of this approach is that it generates largenumbers of features that densely cover an image over the full range ofscales and locations. For example, for a typical image size of fivehundred by five hundred (500×500) pixels, this processing may give riseto about two thousand (2000) stable features, though this number maydepend upon both image content and choices for various parameters. Therelatively rapid approach for recognition may involve comparing thegenerated features with those extracted from a reference database usinga Euclidean distance as a measure of proximity to the reference image.However this method may result in low accuracy. Multiclass SVM maytherefore be utilized to increase the accuracy of the matching by whicheach individual hand pose may be represented and considered as a class.

The following pseudo text process represents an example of Kanade,Lucas, and Tomasi (KLT) flock detection. It is understood that thefollowing pseudo text process may be implemented in any syntaxappropriate for a given implementation. It is further understood thatany other pixel intensity/displacement technique may be used asappropriate for a given implementation.

Initialization Processing:

-   -   1. Learn color histogram;    -   2. Identify n*k features to track with minimum distance;    -   3. Rank the identified features based on color and fixed hand        mask; and    -   4. Select the n highest-ranked features tracking;

Flock Detection Processing:

-   -   1. Update KLT feature locations with image pyramids    -   2. Compute median feature    -   3. For each feature do:        -   If:            -   a) Less than min_dist from any other feature, or            -   b) Outside a max range, centered at median, or            -   c) Low match correlation        -   Then:            -   Relocate feature onto good color spot that meets the                flocking conditions

As can be seen from the above pseudo text processing, initializationincludes learning a color histogram, identifying a set of features totrack with minimum distance between the identified features, ranking theidentified feature set, and selecting a subset of highest-rankedfeatures for tracking. After the initialization processing is completed,the flock detection processing may begin. The flock detection processingincludes updating KLT feature locations with image pyramids andcomputing a median feature. For each median feature, conditionalprocessing may be performed. For example, if the respective feature isless than the defined minimum distance (min_dist) from any otherfeature, is outside a maximum (max) range centered at computed median,or has a low match correlation, then the feature may be relocated onto acolor spot within the color histogram that meets the flockingconditions. In response to this processing, flock detection within animage may be performed.

Use of Trained Multiclass SVM to Infer Pose Type

Regarding use of trained multiclass SVM to infer a pose type, aone-to-one mapping of instances of an element with labels that are drawnfrom a finite set of elements may be established to achieve a form oflearning or inference of a pose type.

SVM may be considered a method of solving binary classification problems(e.g., problems in which the set of possible labels is of size two).Multiclass SVM extends this theory into a multiclass domain. It isrecognized that conventional approaches to solving multiclass problemsusing support vector machines by reducing a single multiclass probleminto multiple binary problems may not be practical for discriminatingbetween hundreds of different hand pose types. The present subjectmatter discriminates a hand pose by detecting salient features withintraining and input images followed by mapping a one-to-onecorrespondence between each feature detected.

This one-to-one mapping allows matching the features across multiple 2Dimages, and additionally allows mapping across a 3D training model usedto generate a training set. This information may then be utilized foroptimizing pose inference at a later stage of the processing, asdescribed in more detail below. As such, SIFT features may not onlyprovide a localized description of the region of interest (ROI) but mayalso provide an idea of a global position of the region of interestespecially when mapped to the 3D training model. As such, the domain ofinterest that results is highly structured and interconnected such thatpositions of features and their relationship to other features inmultiple images may also provide additional information via use of amulticlass SVM designed for interdependent and structured output spaces.

The classification problem may be formulated as follows. A training setis exemplified within Equation (1) below.

(x₁,y₁) . . . (x_(n),y_(n))with labels y_(i) in [1 . . . k]  Equation(1)

where x₁ is a set of m SIFT features [t₁ . . . t_(m)] with the variable“y” representing a vertical coordinate position of the descriptor, thevariable “m” representing the number of SIFT features, and krepresenting the number of labels that denote various pose types. Thevariable “n” represents the size of the SIFT descriptor to process, andthe variable “t” represents the complete feature vector (x₁, y₁) . . .(x_(n),y_(n)).

The approach of this method is to solve the optimization problemreferenced below in Equation (2).

$\begin{matrix}{{\min \mspace{11mu} {1/2}{\sum\limits_{i = {1\mspace{11mu} \ldots \mspace{14mu} k}}\; {{wi}*{wi}}}} + {{C/n}{\sum\limits_{i = {1\mspace{11mu} \ldots \mspace{14mu} n}}\; {\delta \; i}}}} & {{Equation}\mspace{14mu} (2)}\end{matrix}$

-   -   subject to: for all y[1 . . .        k]:[x₁w_(y1)]>=[x₁w_(y)]+100*Δ(y₁,y)−δ₁    -   and for all y[1 . . .        k]:[x_(n)w_(yn)]>=[x_(n)w_(y)]+100*Δ(y_(n),y)−δ_(n)

The constant “C” represents a regularization parameter that trades offmargin size and training error. The element Δ(y_(n),y) represents a lossfunction that returns zero (0) if y_(n) equals y, and 1 otherwise. Thevariable “w” represents an initial weight parameter that depends on thedistance of the pixel (x,y) to the location of the joints within theactually 3D mocap data, the variable “n” represents the size of thedescriptor, the variable “k” represents a number of labels that definethe various hand poses, and “y” represents the vertical coordinateposition of the description in the image.

Regarding database sampling, obtaining a suitable set of training dataimproves the accuracy of an inference method. A small database thatuniformly samples all natural hand configurations and that excludesredundant samples may be preferred as appropriate for a givenimplementation. Training for the multiclass SVM described herein may beperformed using an iterative approach.

For example, a suitable training set of, for example, four thousand(4000) hand images extracted from video frames obtained from anyavailable motion capture (mocap) database may be collected. Such datamay also include three dimensional (3D) joint data as well as 2Dsynthesized images which may be used to establish correspondences and/orcorrelations that increase pose inference accuracy. Each set may bedivided into sets of two for training and testing purposes. As such,processing may begin, for example, with a set of one hundred (100)images. Set counts may then be increased by one hundred (100) images foreach iteration. At each iteration, a root mean square error may bemeasured between test labels. In such an implementation, a set of as fewas one thousand four hundred (1400) images may be utilized in a sampledatabase to yield acceptable results, again as appropriate for a givenimplementation.

Regarding training parameters, results may be optimized for input to anIK solver, and centroids may be calculated for each syntheticallygenerated training image. These synthetically generated training imageand calculated centroids may be associated with joint data from a 3Dmocap database, such as described above. Training and extraction of afeature vector, such as a feature vector of 60 elements, may be used.Such a numeric quantity represents a heuristic estimate that may be usedto eliminate the effect of outlier data elements in a given featurespace. A regularization parameter may be used within a given multiclassSVM implementation to reduce/minimize an effect of bias in the dataset.An example regularization parameter may include, for example, seventyeight one hundredths (0.78). This value may be determined by iterativelytraining the multiclass SVM with incrementing regularization valuesuntil the root mean square (RMS) value of error is less than a desirederror level, such as for example one tenth (0.1).

Approximation of an Articulated Pose Using Inverse Kinematic (IK)Optimization

Regarding approximation of an articulated pose using IK optimization,inverse kinematics may be used to improve articulated pose. As describedabove, the present subject matter does not rely on color gloves.However, it is noted that the present subject matter may adapted to beutilized with gloved hands during cold weather, for example. With thepresent examples, bare-hand pose identification is performed. Centroidsof SIFT descriptors are used to improve accuracy of pose estimation. Itshould be noted that, though processing without IK optimization may beable to distinguish ten (10) or more different pose types consistently,IK optimization allows removal of certain ambiguities in pose that pureSVM implementation may not resolve.

As such, an initial portion of the processing establishes a one-to-onemapping between the 3D pose data (e.g., from a mocap database) and the2D SIFT features that have been detected. The image is broken up intothirty two by thirty two (32×32) image regions, which may be consideredpixel patches for purposes of description. Features are extracted foreach region separately. For each region, centroids of the featureswithin the region are calculated and then that location is mapped to thecorresponding 3D pose data. As a result, for any centroid feature withinthe training set, a three dimensional point on the real hand data may beidentified.

During analysis of the features of the 32×32 pixel patches, the centroidmay again be calculated for the features of each 32×32 pixel patch.Variances from the each centroid to the closest match in the trainingdatabase may be compared and a determination may be made as to which ofthe joint constraints (e.g., hand bone joint constraints) may affect theIK processing.

Each feature centroid may then be mapped to its closest joint stored inthe 3D mocap database data. From this mapping, the IK processing maydetermine the final position of the articulated hand such that thedistance of the joints from that of the training image is minimized. Dueto the complex nature of the joints within a hand, direct analyticalcalculation to get a closed form solution may be complex and may becomputationally expensive in time. As such, a numerical technique toiteratively converge to an optimum solution may be utilized. Real timeperformance limitations may limit a number of iterations that may beperformed for any given implementation. However, it is noted thatprocessing may be resolved with reasonable accuracy (e.g., minimized)within fifty (50) iterations for certain implementations.

Accordingly, the centroids extracted from the 2D training sets may bemapped to their closest joints in the 3D mocap database data. Inresponse to detection of an input image, a centroid may be extracted. Aclosest match of pose type may be inferred based on SVM results. Foreach pose type in the database, 3D joint data may be used as constraintsfor the IK processing. There is a one-to-one correspondence betweenfeatures matched between the input image and the 2D training image,which in turn allows for a determination of a relationship to the 3Djoints in the 3D mocap database data. Using this information, theproblem of optimization decomposes into the following formulationrepresented within Equation (3) through Equation (5), followed byexample pseudo code for iterative processing of these equations.

For a given input image where the feature centroids are consideredtarget degrees of freedom (DOF) to obtain from a 2D training pose:

$\begin{matrix}{g = \left\{ {I_{0}\mspace{11mu} \ldots \mspace{14mu} I_{n}} \right\}} & {{Equation}\mspace{14mu} (3)}\end{matrix}$

The result “g” represents the set containing all of the “n” jointsobtained from the 3D mocap data, which may be considered a “groundtruth” position of the hand pose (e.g., a known truthful orientation ofthe joints), the variable “I” represents a vector depicting theindividual joint position and orientation, and the variable “n”represents the number of joints in the 3D mocap data model.

With Φ representing a current set of joints for the inferred trainingpose.

and

$\begin{matrix}{e = \left\{ {C_{0}\mspace{11mu} \ldots \mspace{14mu} C_{n}} \right\}} & {{Equation}\mspace{14mu} (4)}\end{matrix}$

The result “e” represents the set containing all the “n” joints inferredfrom the detection phase, while the variable “C” represents the vectorrepresenting the orientation and position of individual joints, and thevariable “n” represents the number of joints in the hand model, with theresult representing the inferred training pose features as the currentend effectors. The current end effectors may be considered as the jointsof the inferred training pose. The orientation and position of thecurrent end effectors is changed iteratively until a difference betweentheir position and the joints in the ground truth joint position isminimized.

To minimize error defined as:

α=√{square root over (e−g)}  Equation (5)

The result sigma represents the minimized error.

Below are example iterative steps that may be used to solve the problem,represented in pseudo code form.

While (α<threshold) {   Compute J(e,Φ) //for the current pose   ComputeJ⁻¹//invert the Jacobian matrix   Δe = β(g − e)//select approximate step  ΔΦ = J⁻¹ · Δe//apply change to DOF's   Φ = Φ + ΔΦ//apply change toDOF's   Compute new e vector }

As can be seen from this example pseudo code, the processing iterates aslong as the error is below a defined/configured threshold. The iterativeprocessing includes computing a Jacobian matrix of inferred trainingpose features vector for the current inferred training pose. TheJacobian matrix is then inverted. An approximation is selected for thecurrent set of joints. The selected approximation is applied todetermine a change to the target degrees of freedom (DOF) for thecurrent set of joints. The change to the target degrees of freedom (DOF)for the current set of joints is applied to the current joints in thehand model that were initialized to the inferred training pose. A newerror vector is calculated with respect to the ground truth position ofjoints obtained from the motion capture data. As described above, theprocessing iterates as long as the error is below a defined/configuredthreshold. It is noted that the IK implementation may operate as aseparate process that runs concurrently with the detection system,though these processing operations may be integrated into one process.

FIG. 3 through FIG. 7 below describe example processes that may beexecuted by such devices, such as the television 100, to perform theautomated real time hand tracking, pose classification, and interfacecontrol associated with the present subject matter. Many othervariations on the example processes are possible and all are consideredwithin the scope of the present subject matter. The example processesmay be performed by modules, such as the hand tracking and poseprocessing module 216 and/or executed by the processor 202, associatedwith such devices. It should be noted that time out procedures and othererror control procedures are not illustrated within the exampleprocesses described below for ease of illustration purposes. However, itis understood that all such procedures are considered to be within thescope of the present subject matter.

FIG. 3 is a flow chart of an example of an implementation of a process300 that provides automated real time hand tracking, poseclassification, and interface control. The process 300 starts at 302. Atblock 304, the process 300 extracts, via an image processing module ofan electronics device, a feature set associated with hand gesturedetection and hand pose inference from at least one input image. Atblock 306, the process 300 infers a hand pose type using a trainedmulticlass support vector machine (SVM). At block 308, the process 300approximates the hand pose using inverse kinematics (IK) optimization.

FIG. 4 is a flow chart of an example of an implementation of a process400 that provides training processing associated with automated realtime hand tracking, pose classification, and interface control. Theprocess 400 starts at 402. At decision point 404, the process 400 makesa determination as to whether a training mode has been initiated, suchas via user input, first-time power up, or other form of initiation ofentry into a training mode for a consumer electronics device. Inresponse to determination that the training mode has been initiated, theprocess 400 obtains 3D motion capture (mocap) data, such as describedabove, at block 406. Obtaining the mocap data may include retrieving atext file containing position and orientation of joints for each frameof an animation. An example mocap file may include a sequence ofhundreds of frames that depict one complete movement of a hand.

At block 408, the process 400 renders and animates the sequence offrames associated with the mocap data. Rendering and animating thesequence of frames associated with mocap data may include reading themocap data file frame by frame and creating a 3D mesh that looks like ahand, where the position and orientation of the hand is determined bythe joint data stored in the mocap data file for each frame. Renderingand animating the sequence of frames associated with mocap data mayinclude rendering and animating the mocap data using, for example,OpenGL® or other graphics library.

At block 410, the process 400 extracts 2D images from the 3D renderedoutput of the animation. For example, 2D portable network graphics (PNG)images may be created for each 3D rendered frame of the mocap data. The2D PNG images represent color images of each frame of the 3D renderedoutput saved to an image file similar in format to a camera picture.

At block 412, the process 400 converts the extracted 2D images tograyscale. Each 2D image may be converted into a black and white formatimage. As such, for each image only the intensity information is keptand all additional color information may be discarded. Discarding theadditional color information may help to reduce effects of noise in themocap data or the extracted 2D image data.

At block 414, the process 400 extracts scale invariant featuretransforms (SIFT) features from the 2D grayscale images. As describedabove, SIFT represents a technique that may be used to obtain featuresfrom an image that do not change when subjected to rotation,translation, and scaling. Features may be described as a point on the 2Dimage represented by coordinates, such as “x” and “y” position within animage. For every image extracted from the mocap training data, thisprocessing may be applied to obtain an array of coordinates (e.g., x,yposition) in the image that are locations that do not change for a handfrom frame to frame. Examples of such regions include tips of a finger,lines formed when fingers are curled, or other such regions.

At block 416, the process 400 calculates a centroid and collates thejoint data to create a feature vector. For example, the centroid may becalculated from the coordinates (e.g., the x,y position points) detectedwithin the image. This may include calculating an average (e.g., x1+x2+. . . +xn/n, where n is the number of points) over the array ofcoordinates. Additionally, the distance of the centroid from each thejoints of the hand may be calculated. The joint information may beobtained from the original 3D mocap data. If only the coordinates (e.g.,x,y position) of the joint are used, the distance may be calculated asan approximation. However, this approximation represents a unique valuethat may be used to identify differences between a current pose and the3D mocap data. For the 3D mocap data, there are often sixteen (16)joints, each depicting the effect of a bone in the hand, for examplephalanges, the middle bone in a finger, or other bones. Accordingly,this processing yields a feature vector that is an array of sixty (60)coordinates (e.g., (x,y) positions). The feature vector may be obtainedfrom the SIFT extraction, a centroid position (e.g., (cx,cy)), and thejoint centroid distance which may be represented as an array ofdistances (e.g., {d1, d2, d3 . . . . d16}). It should be noted, however,that this example represents only one possible format of the featurevector.

At block 418, the process 400 processes the feature vector as input intoa multiclass SVM. As described above, SVM stands for support vectormachine and represents a supervised learning method during the trainingphase. The processing is performed by providing a feature vector and alabel depicting a type for the feature vector. For example, a featurevector may be provided for a hand with a palm facing the camera. Such afeature vector may have a label assigned to it as “palm front.”Similarly, this processing may be performed for a quantity, such as twohundred (200) feature vectors, with each feature vector being assignedsuch a label. Upon completion of processing of all available featurevectors, this processing may be executed for several hours using variousparameters to, for example, analyze a number and define an equation thathas the feature vectors as its variables, and that may be used toevaluate to a numerical value that is unique for each label. These typesof equations represent multivariate equations. As such, a multiclass SVMtakes in data in forms of numbers and attempts to approximate anequation that generated those numbers while being constrained by rulesthat each of the numbers must fall into one of the assigned labels.

At block 420, the process 400 tests the trained multiclass SVM kernelwith test data. After the training process is completed, the trainedmulticlass SVM may be referred to as a “kernel.” Test data may be inputinto this kernel for evaluation. The test data may include featurevectors that have been pre-labeled, which may allow tracking that thecorrect label results from the trained multiclass SVM kernel. Forexample, for a hand feature vector, an input feature vector with allfingers closed may be assigned a pre-label of “closed hand” for thispose. Feeding this feature vector into the trained multiclass SVM kernelmay be performed and the results checked to determine whether theprediction returned from the trained multiclass SVM kernel is a label of“closed hand.” If the label “closed hand” is returned, the trainedmulticlass SVM kernel predicted the pose correctly. If the label “closedhand” is not returned, then the prediction may be considered incorrect,and further training may be performed. It should additionally be notedthat, for any given quantity, again for example two hundred (200)feature vectors trained, that quantity of feature vectors may be testedto obtain a percentage value of accuracy. Training may continue untilthe accuracy of prediction achieves a target accuracy level, such as forexample, eighty five to ninety percent (85-90%) accuracy.

As such, at decision point 422, the process 400 makes a determination asto whether the target accuracy level has been achieved. In response todetermining that the target accuracy level has not been achieved, theprocess 400 returns to block 420 to process more test data and iteratesas described above. In response to determining that the target accuracylevel has been achieved, the process 400 returns to decision point 404to await a new request to enter training mode.

Accordingly, the process 400 obtains 3D mocap data, and renders andanimates that data. The process 400 extracts 2D images from the renderedand animated 3D mocap data. The process 400 extracts SIFT features fromgrayscale-converted 2D images, calculates a centroid, and correlatesjoint data to create a feature vector. The process 400 processes featurevector(s) as input into multiclass SVM and tests a resultant trainedmulticlass SVM with test data.

FIG. 5 is a flow chart of an example of an implementation of a process500 that provides detection and pose recognition processing associatedwith automated real time hand tracking, pose classification, andinterface control. The process 500 starts at 502. At decision point 504,the process 500 makes a determination as to whether to begin poseanalysis including pose detection and recognition. Pose analysis maybegin for example, in response to detection of motion via a camera, suchas the camera 112, or in response to detection of a user input requestassociated with analysis. For purposes of the present example, it isassumed that the camera 112 is not initialized and that thedetermination to begin pose analysis is followed by processing toinitialize the camera 112.

As such, in response to determining to begin pose analysis, the process500 initializes the camera 112 and captures a single frame at block 506.At block 508, the process 500 generates a color histogram for imagewithin the captured frame. At block 510, the process 500 assignsprobability values, based upon known hand color histogram informationfor each pixel within the image, of a likelihood of each pixel forming aportion of a hand. This processing allows localization of the hand frombackground image content. At block 512, the process 500 places atracking point on pixels that have very high probability of forming aportion of the hand. These tracking points represent the features thatwill be tracked across subsequent frames.

At block 514, the process 500 converts the image from color to grayscaleand applies a Gaussian filter at different scales to the image. Thisprocessing may be performed for both the pixel intensity/displacementand the SIFT feature extraction in one loop to improve processing speed.At block 516, the process 500 reduces the scale of the image by tenpercent (10%) and re-calculates the probabilities for each pixel, asdescribed above. This processing creates an image pyramid and reducesthe effect of noise. This processing also allows tracking featuresacross varying depths and distances as the hand moves away from, orcloser to, the camera.

At block 518, the process 500 calculates a difference of Gaussianfunction to locate the interest points using the results from theGaussian filtering in different scales and calculates a centroid for theimage. Interest points may be represented by coordinates (e.g., x,ypoints) in the grayscale image. From this interest point information,SIFT keypoints may be calculated using a mechanism, such as orientationassignment, which basically involves assigning a consistent orientationto each keypoint based on local image properties, for example bycalculating the inverse tangent (tan) of the pixel and its neighboringpixels, and removing edge response. The tracking points placed in theimage may use the identified interest point(s) to calculate the centroidof the image. The centroid of the image may be assumed for purposes ofexample to be the center of a detected hand.

At block 520, the process 500 creates a feature vector using thedetected SIFT keypoints. The feature vector may include, for example,the best sixty (60) points obtained from the image. The feature vectormay be stored as an array, such as within the hand tracking and poseprocessing storage area 212 of the memory 208. The centroid may becalculated as a result of pixel intensity/displacement processing andmay be added to this array. Distances from the centroid may becalculated from the top sixteen (16) SIFT point locations. Thisprocessing creates a feature vector similar to the one formed during thetraining phase described above. The SIFT features used to calculate thejoint distances may not form an exact match. However, the SIFT featuresprovide a reasonable approximation of edges formed due to bending offingers because joints represent points that usually show discernablechange in contrast.

At block 522, the process 500 inputs the feature vector into the trainedmulticlass SVM kernel, such as the support vector machine trained duringthe training phase/mode described in association with FIG. 4 above. Asdescribed above, the multiclass SVM represents a complex mathematicalfunction that approximately defines the data it was trained with usingthe feature vector as parameters. Because the function is alreadydefined by the training phase/mode, the result may be calculated rapidlyin response to sending the feature array to the multiclass SVM.

At block 524, the process 500 receives the result from the multiclassSVM kernel. The result may be returned as one of a quantity (e.g., eight(8)) of labels used to train the multiclass SVM. The quantity chosen maybe considered to represent the number of distinct hand positions thatmay be detected. As such, with an example of eight (8) labels, theprocess 500 may detect eight (8) distinct hand positions. Increasedtraining processing and label quantity may increase the number ofdistinct hand positions that may be detected.

At block 526, the process 500 further processes the results using aninverse kinematic (IK) solver. Because the label is known, the process500 has an approximation/estimation of the hand pose given that eachlabel is associated with one hand pose. The position of each of thejoints in this detected pose is also known. Further noting that it isassumed that the final pose was detected using the top sixteen (16) SIFTdescriptors (which are assumed to be the final position of the joints),the original hand pose joints may be moved to the same position wherethe SIFT points are located. This processing is performed using the IKSolver.

Because the joints are connected to each other and have fixedconstrained relationships which each other and varying degrees offreedom (DOF), the movement of the original hand pose to the SIFTkeypoint locations using an IK solver may be done so that theconstraints are satisfied. It should be noted that often the locationswill not match exactly. As such, the IK Solver closely approximates thejoint locations and stops when it is believed the joints are as close tothe final position as the IK solver processing may determine. This stageor processing may be considered the final pose of the hand. The outputof the IK solver may include a quantity (e.g., sixteen (16)) of numbersthat depicts the joint location and orientation. It should be notedthat, though not depicted within the process 500, the joint location andorientation may be used to animate the 3D hand and may result in thefinal pose shown on the screen, such as the display 104. The process 500returns to decision point 504 and awaits another indication to beginpose analysis.

As such, the process 500 initializes a camera and captures a singleframe. Utilizing the single frame the process 500 generates a colorhistogram and assigns a probability to each pixel of a likelihood of thepixel forming a portion of a hand. A tracking point is placed on eachpixel and the image is converted to grayscale. The grayscale image isiteratively reduced and the probabilities are re-calculated. Adifference of Gaussian function is calculated to locate interest points.SIFT keypoints are calculated and a feature vector is created using theSIFT keypoints. The feature vector is input into a trained multiclassSVM and the results are processed using an IK solver to map the resultsto a final hand position within the trained hand data.

FIG. 6 is a flow chart of an example of an implementation of a process600 that provides electronic device user interface processing associatedwith automated real time hand tracking, pose classification, andinterface control. The process 600 starts at 602. At block 604, theprocess 600 detects, via an image processing module of the electronicsdevice, a hand gesture via a camera input. At block 606, the process 600identifies the detected hand gesture from a vocabulary of hand gestures.At block 608, the process 600 controls the electronics device inresponse to the identified hand gesture.

FIG. 7 is a flow chart of an example of an implementation of a process700 that provides electronic device user interface processing and poseassignment to control functions of an electronic device associated withautomated real time hand tracking, pose classification, and interfacecontrol. The process 700 starts at 702. At decision point 704, theprocess 700 makes a determination as to whether a gesture has beendetected. It is understood that processing, such as that described abovein association with FIG. 5, may be used to determine whether a gesturehas been detected. Detecting the hand gesture may include detecting abare-hand position, as described above. Further, detecting the handgesture may include detecting a sequence of bare-hand positions.

In response to determining that a gesture has been detected, the process700 identifies the detected gesture at block 706. The detected handgesture may be identified, for example, from a vocabulary of handgestures.

At decision point 708, the process 700 makes a determination as towhether the identified gesture is associated with a control function ofan electronics device. For example, a hand gesture may be associatedwith turning on the electronics device, turning off the electronicsdevice, adjusting a volume of an audio output, or other control functionof the electronics device.

In response to determining that the identified gesture is associatedwith a control function of an electronics device, the process 700 makesa determination as to whether the hand gesture has been detected for athreshold duration of time associated with control of the electronicsdevice at decision point 710. A control threshold may be used, forexample, to implement hysteresis to the electronic device controls toeliminate false control signals. The configured control threshold mayinclude any suitable range for a given implementation, such as forexample, two hundred milliseconds (200 ms) or a higher or lower durationas appropriate for a given implementation.

In response to determining that the hand gesture has been detected for athreshold duration of time associated with control of the electronicsdevice, the process 700 controls the electronics device in response tothe identified hand gesture at block 712. For example, controlling theelectronics device in response to the identified hand gesture mayinclude turning the electronics device on, turning the electronicsdevice off, adjusting an output volume, or any other control functionappropriate for a given implementation.

In response to completion of controlling the electronics device inresponse to the identified hand gesture at block 712 or in response todetermining that the hand gesture has not been detected for a thresholdduration of time at decision point 710, the process 700 returns todecision point 704 to await detection of another hand gesture anditerates as described above.

Returning to the description of decision point 708, in response todetermining that the identified gesture is not associated with a controlfunction of an electronics device, the process 700 makes a determinationat decision point 714 as to whether an indication has been detectedinstructing assignment of the identified hand gestured to a controlfunction of the electronics device. For example, a separate input, suchas via a remote control device (not shown) or touchscreen input (notshown), may be used to indicate assignment of the identified gesture toa control function of the electronics device. Alternatively, asdescribed above, the indication may be received as an identifiedgesture. In such an implementation, additional processing may beperformed as described above in association with decision point 704 andblock 706 to detect and identify a second hand gesture to be used as theassigned hand gesture for the control function of the electronicsdevice. This additional processing may be considered a portion of theprocessing at decision point 714. In either implementation, in responseto determining that a control assignment has been indicated, the process700 assigns the detected gesture (or the second detected gesture asappropriate for the given implementation) to the control function of theelectronics device at block 716. Upon completion of assignment of thedetected gesture (or the second detected gesture) to the controlfunction of the electronics device at block 716, or in response todetermining that an indication has not been detected instructingassignment of the identified hand gestured to a control function atdecision point 714, the process 700 returns to decision point 704 toawait detection of another hand gesture and iterates as described above.

As such, the process 700 detects and identifies hand gestures andcontrols an electronics device based upon the identified hand gesture.The process 700 also processes detected hand gestures to providehysteresis and to avoid false positives related to hand gesturedetection. The process 700 further assigns gestures to control functionsof the electronics device. Many other possibilities exist for handgesture electronic device control processing and all are consideredwithin the scope of the present subject matter.

Thus, in accord with certain implementations, a method of controlling anelectronics device via hand gestures involves detecting, via an imageprocessing module of the electronics device, a bare-hand position via acamera input based upon a detected sequence of bare-hand positions;determining whether the bare-hand position has been detected for athreshold duration of time; identifying the detected bare-hand positionfrom a vocabulary of hand gestures, where the identified bare-handposition includes a hand gesture associated with powering on theelectronics device; and controlling, in response to determining that thebare-hand position has been detected for the threshold duration of time,the electronics device in response to the identified bare-hand positionby powering on the electronics device.

In another implementation, a computer readable storage medium may storeinstructions which, when executed on one or more programmed processors,carry out a process of controlling an electronics device via handgestures involving detecting a bare-hand position via a camera inputbased upon a detected sequence of bare-hand positions; determiningwhether the bare-hand position has been detected for a thresholdduration of time; identifying the detected bare-hand position from avocabulary of hand gestures, where the identified bare-hand positionincludes a hand gesture associated with powering on the electronicsdevice; and controlling, in response to determining that the bare-handposition has been detected for the threshold duration of time, theelectronics device in response to the identified bare-hand position bypowering on the electronics device.

In certain implementations, a method of controlling an electronicsdevice via hand gestures involves detecting, via an image processingmodule of the electronics device, a hand gesture via a camera input;identifying the detected hand gesture from a vocabulary of handgestures; and controlling the electronics device in response to theidentified hand gesture.

In certain implementations, the method of controlling an electronicsdevice via hand gestures involving detecting, via the image processingmodule of the electronics device, the hand gesture via the camera inputinvolves detecting a bare-hand position. In certain implementations, themethod of detecting, via the image processing module of the electronicsdevice, the hand gesture via the camera input involves detecting asequence of bare-hand positions. In certain implementations, theidentified hand gesture includes a hand gesture associated with poweringon the electronics device and the method of controlling the electronicsdevice in response to the identified hand gesture involves powering onthe electronics device. In certain implementations, the identified handgesture includes a hand gesture associated with powering off theelectronics device and the method of controlling the electronics devicein response to the identified hand gesture involves powering off theelectronics device. In certain implementations, the method furtherinvolves determining whether the hand gesture associated with thecontrol of the electronics device has been detected for a thresholdduration of time; and the method of detecting, via the image processingmodule of the electronics device, the hand gesture via the camera inputinvolves detecting the hand gesture associated with the control of theelectronics device for the threshold duration of time. In certainimplementations, the method further involves determining whether thehand gesture associated with the control of the electronics device hasbeen detected for a threshold duration of time; and the method ofidentifying the detected hand gesture from the vocabulary of handgestures involves identifying the detected hand gesture from thevocabulary of hand gestures in response to determining that the handgesture associated with the control of the electronics device has beendetected for the threshold duration of time. In certain implementations,the method further involves detecting user input indicating assignmentof one of the vocabulary of hand gestures to a control function of theelectronics device; and assigning the one of the vocabulary of handgestures to the control function of the electronics device. In certainimplementations, the method of detecting the user input indicating theassignment of the one of the vocabulary of hand gestures to the controlfunction of the electronics device involves detecting a hand gestureassociated with the assignment of the one of the vocabulary of handgestures to the control function of the electronics device.

In another implementation, a computer readable storage medium may storeinstructions which, when executed on one or more programmed processors,carry out a process of controlling an electronics device via handgestures involving detecting a hand gesture via a camera input;identifying the detected hand gesture from a vocabulary of handgestures; and controlling the electronics device in response to theidentified hand gesture.

In certain implementations, a method of hand position detection involvesextracting, via an image processing module of an electronics device, afeature set associated with hand gesture detection and hand poseinference from a plurality of input images by tracking a region ofinterest (ROI) between subsequent video frames of the plurality of inputimages as a flock of features; triggering scale invariant featuretransforms (SIFT) feature extraction; calculating an optical flow pathof the flock of features; measuring brightness gradients in multipledirections across the plurality of input images; generating imagepyramids from the measured brightness gradients; extracting pixelintensity/displacement features and the SIFT features using thegenerated image pyramids; and applying a cascade filter in associationwith extracting the pixel intensity/displacement features and the SIFTfeatures from the generated image pyramids; the method involvesinferring a hand pose type using a trained multiclass support vectormachine (SVM) by detecting at least one feature within a training imageand the plurality of input images; and performing a one-to-one mappingof instances of the at least one feature within the plurality of inputimages with at least one label drawn from a finite set of elements,where the at least one label includes at least one label generatedduring a training phase based upon a motion capture three dimensional(3D) data set; and the method involves approximating the hand pose usinginverse kinematics (IK) optimization by partitioning the plurality ofinput images into a plurality of processing regions; determining acentroid of features within each of the plurality of processing regions;mapping a location of each feature centroid onto three dimensional (3D)pose data associated with a motion capture data set; comparing variancesfrom each feature centroid to a closest match within the 3D pose data;determining which of a plurality of joint constraints affect the IKoptimization; mapping each feature centroid to a closest joint storedwithin the 3D pose data; minimizing a distance of each mapped closestjoint within the training image based upon the 3D pose data; anddetermining a final hand position based upon the minimized distance ofeach mapped closest joint within the training image.

In another implementation, a computer readable storage medium may storeinstructions which, when executed on one or more programmed processors,carry out a process of hand position detection involving extracting, viaan image processing module of an electronics device, a feature setassociated with hand gesture detection and hand pose inference from aplurality of input images by tracking a region of interest (ROI) betweensubsequent video frames of the plurality of input images as a flock offeatures; triggering scale invariant feature transforms (SIFT) featureextraction; calculating an optical flow path of the flock of features;measuring brightness gradients in multiple directions across theplurality of input images; generating image pyramids from the measuredbrightness gradients; extracting pixel intensity/displacement featuresand the SIFT features using the generated image pyramids; and applying acascade filter in association with extracting the pixelintensity/displacement features and the SIFT features from the generatedimage pyramids; the process involving inferring a hand pose type using atrained multiclass support vector machine (SVM) by detecting at leastone feature within a training image and the plurality of input images;and performing a one-to-one mapping of instances of the at least onefeature within the plurality of input images with at least one labeldrawn from a finite set of elements, where the at least one labelincludes at least one label generated during a training phase based upona motion capture three dimensional (3D) data set; and the processinvolving approximating the hand pose using inverse kinematics (IK)optimization by partitioning the plurality of input images into aplurality of processing regions; determining a centroid of featureswithin each of the plurality of processing regions; mapping a locationof each feature centroid onto three dimensional (3D) pose dataassociated with a motion capture data set; comparing variances from eachfeature centroid to a closest match within the 3D pose data; determiningwhich of a plurality of joint constraints affect the IK optimization;mapping each feature centroid to a closest joint stored within the 3Dpose data; minimizing a distance of each mapped closest joint within thetraining image based upon the 3D pose data; and determining a final handposition based upon the minimized distance of each mapped closest jointwithin the training image.

In certain implementations, a method of hand position detection involvesextracting, via an image processing module of an electronics device, afeature set associated with hand gesture detection and hand poseinference from at least one input image; inferring a hand pose typeusing a trained multiclass support vector machine (SVM); andapproximating the hand pose using inverse kinematics (IK) optimization.

In certain implementations, the method of hand position detectioninvolving extracting, via the image processing module of the electronicsdevice, the feature set associated with hand gesture detection and handpose inference from the at least one input image involves measuringbrightness gradients in multiple directions across the at least oneinput image; and generating image pyramids from the measured brightnessgradients. In certain implementations, the method further involvesextracting pixel intensity/displacement features and scale invariantfeature transforms (SIFT) features using the generated image pyramids.In certain implementations, the method further involves applying acascade filter in association with extracting the pixelintensity/displacement features and the SIFT features from the generatedimage pyramids. In certain implementations, the at least one input imageincludes a plurality of input images, and the method of extracting, viathe image processing module of the electronics device, the feature setassociated with hand gesture detection and hand pose inference from theat least one input image involves tracking a region of interest (ROI)between subsequent video frames of the plurality of input images as aflock of features; triggering scale invariant feature transforms (SIFT)feature extraction; and calculating an optical flow path of the flock offeatures. In certain implementations, the method of tracking the ROIbetween subsequent video frames of the plurality of input images as theflock of features involves tracking a two dimensional (2D) combinationof pixel intensity/displacement features and a learned foreground colordistribution. In certain implementations, the method of calculating theoptical flow path of the flock of features involves applying at leastone constraint on each of the flock of features such that the flock offeatures maintain a minimum distance from each other. In certainimplementations, the method of inferring a hand pose type using thetrained multiclass SVM involves detecting at least one feature within atraining image and the at least one input image; and performing aone-to-one mapping of instances of the at least one feature within theat least one input image with at least one label drawn from a finite setof elements. In certain implementations, the at least one label includesat least one label generated during a training phase based upon a motioncapture three dimensional (3D) data set. In certain implementations, themethod of approximating the hand pose using IK optimization involvespartitioning the at least one input image into a plurality of processingregions; determining a centroid of features within each of the pluralityof processing regions; and mapping a location of each feature centroidonto three dimensional (3D) pose data associated with a motion capturedata set. In certain implementations, the method of determining thecentroid of features within each of the plurality of processing regionsinvolves comparing variances from each feature centroid to a closestmatch within the 3D pose data; and determining which of a plurality ofjoint constraints affect the IK optimization. In certainimplementations, the method further involves mapping each featurecentroid to a closest joint stored within the 3D pose data. In certainimplementations, the method further involves minimizing a distance ofeach mapped closest joint within a training image based upon the 3D posedata; and determining a final hand position based upon the minimizeddistance of each mapped closest joint within the training image. Incertain implementations, the method further involves defining aconfigurable resolution threshold for image processing; and adjustingthe configurable resolution threshold. In certain implementations, themethod further involves storing the extracted feature set associatedwith the hand gesture detection and the hand pose inference.

In another implementation, a computer readable storage medium may storeinstructions which, when executed on one or more programmed processors,carry out a process of hand position detection involving extracting afeature set associated with hand gesture detection and hand poseinference from at least one input image; inferring a hand pose typeusing a trained multiclass support vector machine (SVM); andapproximating the hand pose using inverse kinematics (IK) optimization.

An apparatus for controlling an electronics device via hand gesturesconsistent with certain implementations has a camera and a processorprogrammed to detect a bare-hand position via the camera based upon adetected sequence of bare-hand positions; determine whether thebare-hand position has been detected for a threshold duration of time;identify the detected bare-hand position from a vocabulary of handgestures, where the identified bare-hand position includes a handgesture associated with powering on the electronics device; and control,in response to determining that the bare-hand position has been detectedfor the threshold duration of time, the electronics device in responseto the identified bare-hand position by powering on the electronicsdevice.

An apparatus for controlling an electronics device via hand gesturesconsistent with certain implementations has a camera and a processorprogrammed to detect a hand gesture via the camera; identify thedetected hand gesture from a vocabulary of hand gestures; and controlthe electronics device in response to the identified hand gesture.

In certain implementations, in being programmed to detect the handgesture via the camera, the processor is programmed to detect abare-hand position. In certain implementations, in being programmed todetect the hand gesture via the camera, the processor is programmed todetect a sequence of bare-hand positions. In certain implementations,the identified hand gesture includes a hand gesture associated withpowering on the electronics device and, in being programmed to controlthe electronics device in response to the identified hand gesture, theprocessor is programmed to power on the electronics device. In certainimplementations, the identified hand gesture includes a hand gestureassociated with powering off the electronics device and, in beingprogrammed to control the electronics device in response to theidentified hand gesture, the processor is programmed to power off theelectronics device. In certain implementations, the processor is furtherprogrammed to determine whether the hand gesture associated with thecontrol of the electronics device has been detected for a thresholdduration of time; and, in being programmed to detect the hand gesturevia the camera, the processor is programmed to detect the hand gestureassociated with the control of the electronics device for the thresholdduration of time. In certain implementations, the processor is furtherprogrammed to determine whether the hand gesture associated with thecontrol of the electronics device has been detected for a thresholdduration of time; and, in being programmed to identify the detected handgesture from the vocabulary of hand gestures, the processor isprogrammed to identify the detected hand gesture from the vocabulary ofhand gestures in response to determining that the hand gestureassociated with the control of the electronics device has been detectedfor the threshold duration of time. In certain implementations, theprocessor is further programmed to detect user input indicatingassignment of one of the vocabulary of hand gestures to a controlfunction of the electronics device; and assign the one of the vocabularyof hand gestures to the control function of the electronics device. Incertain implementations, in being programmed to detect the user inputindicating the assignment of the one of the vocabulary of hand gesturesto the control function of the electronics device, the processor isprogrammed to detect a hand gesture associated with the assignment ofthe one of the vocabulary of hand gestures to the control function ofthe electronics device.

An apparatus for hand position detection consistent with certainimplementations has a camera and a processor programmed to extract afeature set associated with hand gesture detection and hand poseinference from a plurality of input images received via the camera, theprocessor being further programmed to track a region of interest (ROI)between subsequent video frames of the plurality of input images as aflock of features; trigger scale invariant feature transforms (SIFT)feature extraction; calculate an optical flow path of the flock offeatures; measure brightness gradients in multiple directions across theplurality of input images; generate image pyramids from the measuredbrightness gradients; extract pixel intensity/displacement features andthe SIFT features using the generated image pyramids; and apply acascade filter in association with extracting the pixelintensity/displacement features and the SIFT features from the generatedimage pyramids; the processor is programmed to infer a hand pose typeusing a trained multiclass support vector machine (SVM), the processorbeing further programmed to detect at least one feature within atraining image and the plurality of input images; and perform aone-to-one mapping of instances of the at least one feature within theplurality of input images with at least one label drawn from a finiteset of elements, where the at least one label includes at least onelabel generated during a training phase based upon a motion capturethree dimensional (3D) data set; and the processor is programmed toapproximate the hand pose using inverse kinematics (IK) optimization,the processor being further programmed to partition the plurality ofinput images into a plurality of processing regions; determine acentroid of features within each of the plurality of processing regions;map a location of each feature centroid onto three dimensional (3D) posedata associated with a motion capture data set; compare variances fromeach feature centroid to a closest match within the 3D pose data;determine which of a plurality of joint constraints affect the IKoptimization; map each feature centroid to a closest joint stored withinthe 3D pose data; minimize a distance of each mapped closest jointwithin the training image based upon the 3D pose data; and determine afinal hand position based upon the minimized distance of each mappedclosest joint within the training image.

An apparatus for hand position detection, consistent with certainimplementations, has a camera and a processor programmed to extract afeature set associated with hand gesture detection and hand poseinference from at least one input image received via the camera; infer ahand pose type using a trained multiclass support vector machine (SVM);and approximate the hand pose using inverse kinematics (IK)optimization.

In certain implementations, in being programmed to extract the featureset associated with hand gesture detection and hand pose inference fromthe at least one input image received via the camera, the processor isprogrammed to measure brightness gradients in multiple directions acrossthe at least one input image; and generate image pyramids from themeasured brightness gradients. In certain implementations, the processoris further programmed to extract pixel intensity/displacement featuresand scale invariant feature transforms (SIFT) features using thegenerated image pyramids. In certain implementations, the processor isfurther programmed to apply a cascade filter in association withextracting the pixel intensity/displacement features and the SIFTfeatures from the generated image pyramids. In certain implementations,the at least one input image includes a plurality of input images, and,in being programmed to extract the feature set associated with handgesture detection and hand pose inference from the at least one inputimage received via the camera, the processor is programmed to track aregion of interest (ROI) between subsequent video frames of theplurality of input images as a flock of features; trigger scaleinvariant feature transforms (SIFT) feature extraction; and calculate anoptical flow path of the flock of features. In certain implementations,in being programmed to track the ROI between subsequent video frames ofthe plurality of input images as the flock of features, the processor isprogrammed to track a two dimensional (2D) combination of pixelintensity/displacement features and a learned foreground colordistribution. In certain implementations, in being programmed tocalculate the optical flow path of the flock of features, the processoris programmed to apply at least one constraint on each of the flock offeatures such that the flock of features maintain a minimum distancefrom each other. In certain implementations, in being programmed toinfer a hand pose type using the trained multiclass SVM, the processoris programmed to detect at least one feature within a training image andthe at least one input image; and perform a one-to-one mapping ofinstances of the at least one feature within the at least one inputimage with at least one label drawn from a finite set of elements. Incertain implementations, the at least one label includes at least onelabel generated during a training phase based upon a motion capturethree dimensional (3D) data set. In certain implementations, in beingprogrammed to approximate the hand pose using IK optimization, theprocessor is programmed to partition the at least one input image into aplurality of processing regions; determine a centroid of features withineach of the plurality of processing regions; and map a location of eachfeature centroid onto three dimensional (3D) pose data associated with amotion capture data set. In certain implementations, in being programmedto determine the centroid of features within each of the plurality ofprocessing regions, the processor is programmed to compare variancesfrom each feature centroid to a closest match within the 3D pose data;and determine which of a plurality of joint constraints affect the IKoptimization. In certain implementations, the processor is furtherprogrammed to map each feature centroid to a closest joint stored withinthe 3D pose data. In certain implementations, the processor is furtherprogrammed to minimize a distance of each mapped closest joint within atraining image based upon the 3D pose data; and determine a final handposition based upon the minimized distance of each mapped closest jointwithin the training image. In certain implementations, the processor isfurther programmed to define a configurable resolution threshold forimage processing; and adjust the configurable resolution threshold. Incertain implementations, the apparatus for hand position detection has amemory; and the processor is further programmed to store the extractedfeature set associated with the hand gesture detection and the hand poseinference in the memory.

While certain embodiments herein were described in conjunction withspecific circuitry that carries out the functions described, otherembodiments are contemplated in which the circuit functions are carriedout using equivalent elements executed on one or more programmedprocessors. General purpose computers, microprocessor based computers,micro-controllers, optical computers, analog computers, dedicatedprocessors, application specific circuits and/or dedicated hard wiredlogic and analog circuitry may be used to construct alternativeequivalent embodiments. Other embodiments could be implemented usinghardware component equivalents such as special purpose hardware,dedicated processors or combinations thereof.

Certain embodiments may be implemented using one or more programmedprocessors executing programming instructions that in certain instancesare broadly described above in flow chart form that can be stored on anysuitable electronic or computer readable storage medium (such as, forexample, disc storage, Read Only Memory (ROM) devices, Random AccessMemory (RAM) devices, network memory devices, optical storage elements,magnetic storage elements, magneto-optical storage elements, flashmemory, core memory and/or other equivalent volatile and non-volatilestorage technologies). However, those skilled in the art willappreciate, upon consideration of the present teaching, that theprocesses described above can be implemented in any number of variationsand in many suitable programming languages without departing fromembodiments of the present invention. For example, the order of certainoperations carried out can often be varied, additional operations can beadded or operations can be deleted without departing from certainembodiments of the invention. Error trapping can be added and/orenhanced and variations can be made in user interface and informationpresentation without departing from certain embodiments of the presentinvention. Such variations are contemplated and considered equivalent.

While certain illustrative embodiments have been described, it isevident that many alternatives, modifications, permutations andvariations will become apparent to those skilled in the art in light ofthe foregoing description.

1. A method of controlling an electronics device via hand gestures,comprising: detecting, via an image processing module of the electronicsdevice, a bare-hand position via a camera input based upon a detectedsequence of bare-hand positions; determining whether the bare-handposition has been detected for a threshold duration of time; identifyingthe detected bare-hand position from a vocabulary of hand gestures,where the identified bare-hand position comprises a hand gestureassociated with powering on the electronics device; and controlling, inresponse to determining that the bare-hand position has been detectedfor the threshold duration of time, the electronics device in responseto the identified bare-hand position by powering on the electronicsdevice.
 2. A computer readable storage medium storing instructionswhich, when executed on one or more programmed processors, carry out themethod according to claim
 1. 3. A method of controlling an electronicsdevice via hand gestures, comprising: detecting, via an image processingmodule of the electronics device, a hand gesture via a camera input;identifying the detected hand gesture from a vocabulary of handgestures; and controlling the electronics device in response to theidentified hand gesture.
 4. The method according to claim 3, wheredetecting, via the image processing module of the electronics device,the hand gesture via the camera input comprises detecting a bare-handposition.
 5. The method according to claim 3, where detecting, via theimage processing module of the electronics device, the hand gesture viathe camera input comprises detecting a sequence of bare-hand positions.6. The method according to claim 3, where the identified hand gesturecomprises a hand gesture associated with powering on the electronicsdevice and where controlling the electronics device in response to theidentified hand gesture comprises powering on the electronics device. 7.The method according to claim 3, where the identified hand gesturecomprises a hand gesture associated with powering off the electronicsdevice and where controlling the electronics device in response to theidentified hand gesture comprises powering off the electronics device.8. The method according to claim 3, further comprising: determiningwhether the hand gesture associated with the control of the electronicsdevice has been detected for a threshold duration of time; and wheredetecting, via the image processing module of the electronics device,the hand gesture via the camera input comprises detecting the handgesture associated with the control of the electronics device for thethreshold duration of time.
 9. The method according to claim 3, furthercomprising: determining whether the hand gesture associated with thecontrol of the electronics device has been detected for a thresholdduration of time; and where identifying the detected hand gesture fromthe vocabulary of hand gestures comprises identifying the detected handgesture from the vocabulary of hand gestures in response to determiningthat the hand gesture associated with the control of the electronicsdevice has been detected for the threshold duration of time.
 10. Themethod according to claim 3, further comprising: detecting user inputindicating assignment of one of the vocabulary of hand gestures to acontrol function of the electronics device; and assigning the one of thevocabulary of hand gestures to the control function of the electronicsdevice.
 11. The method according to claim 10, where detecting the userinput indicating the assignment of the one of the vocabulary of handgestures to the control function of the electronics device comprisesdetecting a hand gesture associated with the assignment of the one ofthe vocabulary of hand gestures to the control function of theelectronics device.
 12. A computer readable storage medium storinginstructions which, when executed on one or more programmed processors,carry out the method according to claim
 3. 13. A method of hand positiondetection, comprising: extracting, via an image processing module of anelectronics device, a feature set associated with hand gesture detectionand hand pose inference from a plurality of input images by: tracking aregion of interest (ROI) between subsequent video frames of theplurality of input images as a flock of features; triggering scaleinvariant feature transforms (SIFT) feature extraction; calculating anoptical flow path of the flock of features; measuring brightnessgradients in multiple directions across the plurality of input images;generating image pyramids from the measured brightness gradients;extracting pixel intensity/displacement features and the SIFT featuresusing the generated image pyramids; and applying a cascade filter inassociation with extracting the pixel intensity/displacement featuresand the SIFT features from the generated image pyramids; inferring ahand pose type using a trained multiclass support vector machine (SVM)by: detecting at least one feature within a training image and theplurality of input images; and performing a one-to-one mapping ofinstances of the at least one feature within the plurality of inputimages with at least one label drawn from a finite set of elements,where the at least one label comprises at least one label generatedduring a training phase based upon a motion capture three dimensional(3D) data set; and approximating the hand pose using inverse kinematics(IK) optimization by: partitioning the plurality of input images into aplurality of processing regions; determining a centroid of featureswithin each of the plurality of processing regions; mapping a locationof each feature centroid onto three dimensional (3D) pose dataassociated with a motion capture data set; comparing variances from eachfeature centroid to a closest match within the 3D pose data; determiningwhich of a plurality of joint constraints affect the IK optimization;mapping each feature centroid to a closest joint stored within the 3Dpose data; minimizing a distance of each mapped closest joint within thetraining image based upon the 3D pose data; and determining a final handposition based upon the minimized distance of each mapped closest jointwithin the training image.
 14. A computer readable storage mediumstoring instructions which, when executed on one or more programmedprocessors, carry out the method according to claim
 13. 15. A method ofhand position detection, comprising: extracting, via an image processingmodule of an electronics device, a feature set associated with handgesture detection and hand pose inference from at least one input image;inferring a hand pose type using a trained multiclass support vectormachine (SVM); and approximating the hand pose using inverse kinematics(IK) optimization.
 16. The method according to claim 15, whereextracting, via the image processing module of the electronics device,the feature set associated with hand gesture detection and hand poseinference from the at least one input image comprises: measuringbrightness gradients in multiple directions across the at least oneinput image; and generating image pyramids from the measured brightnessgradients.
 17. The method according to claim 16, further comprising:extracting pixel intensity/displacement features and scale invariantfeature transforms (SIFT) features using the generated image pyramids.18. The method according to claim 17, further comprising applying acascade filter in association with extracting the pixelintensity/displacement features and the SIFT features from the generatedimage pyramids.
 19. The method according to claim 15, where the at leastone input image comprises a plurality of input images, and extracting,via the image processing module of the electronics device, the featureset associated with hand gesture detection and hand pose inference fromthe at least one input image comprises: tracking a region of interest(ROI) between subsequent video frames of the plurality of input imagesas a flock of features; triggering scale invariant feature transforms(SIFT) feature extraction; and calculating an optical flow path of theflock of features.
 20. The method according to claim 19, where trackingthe ROI between subsequent video frames of the plurality of input imagesas the flock of features comprises: tracking a two dimensional (2D)combination of pixel intensity/displacement features and a learnedforeground color distribution.
 21. The method according to claim 19,where calculating the optical flow path of the flock of featurescomprises applying at least one constraint on each of the flock offeatures such that the flock of features maintain a minimum distancefrom each other.
 22. The method according to claim 15, where inferring ahand pose type using the trained multiclass SVM comprises: detecting atleast one feature within a training image and the at least one inputimage; and performing a one-to-one mapping of instances of the at leastone feature within the at least one input image with at least one labeldrawn from a finite set of elements.
 23. The method according to claim22, where the at least one label comprises at least one label generatedduring a training phase based upon a motion capture three dimensional(3D) data set.
 24. The method according to claim 15, where approximatingthe hand pose using IK optimization comprises: partitioning the at leastone input image into a plurality of processing regions; determining acentroid of features within each of the plurality of processing regions;and mapping a location of each feature centroid onto three dimensional(3D) pose data associated with a motion capture data set.
 25. The methodaccording to claim 24, where determining the centroid of features withineach of the plurality of processing regions comprises: comparingvariances from each feature centroid to a closest match within the 3Dpose data; and determining which of a plurality of joint constraintsaffect the IK optimization.
 26. The method according to claim 25,further comprising mapping each feature centroid to a closest jointstored within the 3D pose data.
 27. The method according to claim 26,further comprising: minimizing a distance of each mapped closest jointwithin a training image based upon the 3D pose data; and determining afinal hand position based upon the minimized distance of each mappedclosest joint within the training image.
 28. The method according toclaim 15, further comprising: defining a configurable resolutionthreshold for image processing; and adjusting the configurableresolution threshold.
 29. The method according to claim 15, furthercomprising storing the extracted feature set associated with the handgesture detection and the hand pose inference.
 30. A computer readablestorage medium storing instructions which, when executed on one or moreprogrammed processors, carry out the method according to claim
 15. 31.An apparatus for controlling an electronics device via hand gestures,comprising: a camera; and a processor programmed to: detect a bare-handposition via the camera based upon a detected sequence of bare-handpositions; determine whether the bare-hand position has been detectedfor a threshold duration of time; identify the detected bare-handposition from a vocabulary of hand gestures, where the identifiedbare-hand position comprises a hand gesture associated with powering onthe electronics device; and control, in response to determining that thebare-hand position has been detected for the threshold duration of time,the electronics device in response to the identified bare-hand positionby powering on the electronics device.
 32. An apparatus for controllingan electronics device via hand gestures, comprising: a camera; and aprocessor programmed to: detect a hand gesture via the camera; identifythe detected hand gesture from a vocabulary of hand gestures; andcontrol the electronics device in response to the identified handgesture.
 33. The apparatus according to claim 32, where, in beingprogrammed to detect the hand gesture via the camera, the processor isprogrammed to detect a bare-hand position.
 34. The apparatus accordingto claim 32, where, in being programmed to detect the hand gesture viathe camera, the processor is programmed to detect a sequence ofbare-hand positions.
 35. The apparatus according to claim 32, where theidentified hand gesture comprises a hand gesture associated withpowering on the electronics device and where, in being programmed tocontrol the electronics device in response to the identified handgesture, the processor is programmed to power on the electronics device.36. The apparatus according to claim 32, where the identified handgesture comprises a hand gesture associated with powering off theelectronics device and where, in being programmed to control theelectronics device in response to the identified hand gesture, theprocessor is programmed to power off the electronics device.
 37. Theapparatus according to claim 32, where the processor is furtherprogrammed to: determine whether the hand gesture associated with thecontrol of the electronics device has been detected for a thresholdduration of time; and where, in being programmed to detect the handgesture via the camera, the processor is programmed to detect the handgesture associated with the control of the electronics device for thethreshold duration of time.
 38. The apparatus according to claim 32,where the processor is further programmed to: determine whether the handgesture associated with the control of the electronics device has beendetected for a threshold duration of time; and where, in beingprogrammed to identify the detected hand gesture from the vocabulary ofhand gestures, the processor is programmed to identify the detected handgesture from the vocabulary of hand gestures in response to determiningthat the hand gesture associated with the control of the electronicsdevice has been detected for the threshold duration of time.
 39. Theapparatus according to claim 32, where the processor is furtherprogrammed to: detect user input indicating assignment of one of thevocabulary of hand gestures to a control function of the electronicsdevice; and assign the one of the vocabulary of hand gestures to thecontrol function of the electronics device.
 40. The apparatus accordingto claim 39, where, in being programmed to detect the user inputindicating the assignment of the one of the vocabulary of hand gesturesto the control function of the electronics device, the processor isprogrammed to detect a hand gesture associated with the assignment ofthe one of the vocabulary of hand gestures to the control function ofthe electronics device.
 41. An apparatus for hand position detection,comprising: a camera; and a processor programmed to: extract a featureset associated with hand gesture detection and hand pose inference froma plurality of input images received via the camera, where the processoris further programmed to: track a region of interest (ROI) betweensubsequent video frames of the plurality of input images as a flock offeatures; trigger scale invariant feature transforms (SIFT) featureextraction; calculate an optical flow path of the flock of features;measure brightness gradients in multiple directions across the pluralityof input images; generate image pyramids from the measured brightnessgradients; extract pixel intensity/displacement features and the SIFTfeatures using the generated image pyramids; and apply a cascade filterin association with extracting the pixel intensity/displacement featuresand the SIFT features from the generated image pyramids; infer a handpose type using a trained multiclass support vector machine (SVM), wherethe processor is further programmed to: detect at least one featurewithin a training image and the plurality of input images; and perform aone-to-one mapping of instances of the at least one feature within theplurality of input images with at least one label drawn from a finiteset of elements, where the at least one label comprises at least onelabel generated during a training phase based upon a motion capturethree dimensional (3D) data set; and approximate the hand pose usinginverse kinematics (IK) optimization, where the processor is furtherprogrammed to: partition the plurality of input images into a pluralityof processing regions; determine a centroid of features within each ofthe plurality of processing regions; map a location of each featurecentroid onto three dimensional (3D) pose data associated with a motioncapture data set; compare variances from each feature centroid to aclosest match within the 3D pose data; determine which of a plurality ofjoint constraints affect the IK optimization; map each feature centroidto a closest joint stored within the 3D pose data; minimize a distanceof each mapped closest joint within the training image based upon the 3Dpose data; and determine a final hand position based upon the minimizeddistance of each mapped closest joint within the training image.
 42. Anapparatus for hand position detection, comprising: a camera; and aprocessor programmed to: extract a feature set associated with handgesture detection and hand pose inference from at least one input imagereceived via the camera; infer a hand pose type using a trainedmulticlass support vector machine (SVM); and approximate the hand poseusing inverse kinematics (IK) optimization.
 43. The apparatus accordingto claim 42, where, in being programmed to extract the feature setassociated with hand gesture detection and hand pose inference from theat least one input image received via the camera, the processor isprogrammed to: measure brightness gradients in multiple directionsacross the at least one input image; and generate image pyramids fromthe measured brightness gradients.
 44. The apparatus according to claim43, where the processor is further programmed to: extract pixelintensity/displacement features and scale invariant feature transforms(SIFT) features using the generated image pyramids.
 45. The apparatusaccording to claim 44, where the processor is further programmed toapply a cascade filter in association with extracting the pixelintensity/displacement features and the SIFT features from the generatedimage pyramids.
 46. The apparatus according to claim 42, where the atleast one input image comprises a plurality of input images, and, wherein being programmed to extract the feature set associated with handgesture detection and hand pose inference from the at least one inputimage received via the camera, the processor is programmed to: track aregion of interest (ROI) between subsequent video frames of theplurality of input images as a flock of features; trigger scaleinvariant feature transforms (SIFT) feature extraction; and calculate anoptical flow path of the flock of features.
 47. The apparatus accordingto claim 46, where, in being programmed to track the ROI betweensubsequent video frames of the plurality of input images as the flock offeatures, the processor is programmed to: track a two dimensional (2D)combination of pixel intensity/displacement features and a learnedforeground color distribution.
 48. The apparatus according to claim 46,where, in being programmed to calculate the optical flow path of theflock of features, the processor is programmed to apply at least oneconstraint on each of the flock of features such that the flock offeatures maintain a minimum distance from each other.
 49. The apparatusaccording to claim 42, where, in being programmed to infer a hand posetype using the trained multiclass SVM, the processor is programmed to:detect at least one feature within a training image and the at least oneinput image; and perform a one-to-one mapping of instances of the atleast one feature within the at least one input image with at least onelabel drawn from a finite set of elements.
 50. The apparatus accordingto claim 49, where the at least one label comprises at least one labelgenerated during a training phase based upon a motion capture threedimensional (3D) data set.
 51. The apparatus according to claim 42,where, in being programmed to approximate the hand pose using IKoptimization, the processor is programmed to: partition the at least oneinput image into a plurality of processing regions; determine a centroidof features within each of the plurality of processing regions; and mapa location of each feature centroid onto three dimensional (3D) posedata associated with a motion capture data set.
 52. The apparatusaccording to claim 51, where, in being programmed to determine thecentroid of features within each of the plurality of processing regions,the processor is programmed to: compare variances from each featurecentroid to a closest match within the 3D pose data; and determine whichof a plurality of joint constraints affect the 1K optimization.
 53. Theapparatus according to claim 52, where the processor is furtherprogrammed to map each feature centroid to a closest joint stored withinthe 3D pose data.
 54. The apparatus according to claim 53, where theprocessor is further programmed to: minimize a distance of each mappedclosest joint within a training image based upon the 3D pose data; anddetermine a final hand position based upon the minimized distance ofeach mapped closest joint within the training image.
 55. The apparatusaccording to claim 42, where the processor is further programmed to:define a configurable resolution threshold for image processing; andadjust the configurable resolution threshold.
 56. The apparatusaccording to claim 42, further comprising: a memory; and where theprocessor is further programmed to store the extracted feature setassociated with the hand gesture detection and the hand pose inferencein the memory.